Constrained Close Loop Model Predictive Control Architecture for Deformable Linear Objects for Dynamic Motions

Abstract

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation hinder effective closed-loop manipulation. Building upon our previously established state estimation framework and control architecture, this paper introduces a constrained, closed-loop model predictive control (MPC) approach for highly dynamic DLO manipulation. We integrate hardware and environmental limits into the MPC as soft constraints, enabling tasks like dynamic tracking with endpoint wall avoidance. Additionally, we demonstrate challenging maneuvers, such as swinging a DLO through a narrow slot in open-loop. Furthermore, we enhance our prior state estimation method by incorporating mid-object via points, significantly improving shape reconstruction and enabling the prediction of non-monotonically curved geometries. Finally, we present an extensive evaluation of the complete control architecture using two simulated and five physical DLOs. This analysis assesses the critical influence of kinematic segment discretization, object dynamics, and distribution shifts on overall tracking performance, thereby validating the efficacy and robustness of the approach.